1. Field of the Invention
The present teachings relate to a machine implemented method for estimating the state of charge of a battery. This machine implemented method includes estimating the battery state of charge by use of a multi-layer model of an electrode to derive the state of charge, and utilizing battery voltage and current readings to update diffusion coefficients used by the multi-layer model to obtain a more accurate estimation of the battery state of charge. The method can be implemented on a processing device to control battery charging conditions over a charging regime to optimize the battery charging process.
2. Discussion of the Related Art
A fusion type soft computing algorithm utilized to estimate the state of charge of the battery from inputs including battery current, voltage and temperature is set forth in U.S. Patent Application Publication No. 2007/0005276 A1. Another battery management system that determines the state of charge of the battery by receiving signals such as current or voltage from the battery is discussed in U.S. Patent Application Publication No. 2009/0001992 A1. A multi-layer model of proton diffusion within a battery is discussed in U.S. Pat. No. 6,016,047.
One model used to estimate a battery's internal states is the Single Particle Battery model. This model is presented in detail in, at least, two technical papers, “Cycle Life Modeling of Lithium-Ion Batteries,” G. Ning, B. N. Popov, Journal of Electrochemical Society 151 (2004), pages A1584-A1591, and “Online estimation of the state of charge of a lithium ion cell,” S. Santhanagopalan, R. E. White, Journal of Power Sources 161 (2006), pages 1346-1355, both of which are incorporated herein in their entireties.
The well-known Coulomb-Counting method of estimating state of charge simply accumulates the output of the battery current sensor but is susceptible to sensor error. This sensor and calculation error is addressed by other battery model estimation methods such as those using a Kalman filter. While these methods update the state of charge estimation by comparing the model output and the actual measured cell voltage, problems arise when there is both state of charge estimation error and battery modeling error as will likely occur due to cell variation and aging of the cell. A comparison of a results obtained by a conventional filter method (solid lines) with both an initial state of charge estimation error and initial modeling error versus the actual cell voltage and state of charge (dashed lines) is illustrated in FIG. 1.
A need exists for a method that accurately determines the state of charge of a battery which information can be utilized to control a battery charging system, which can in turn, lead to an improved energy storage device, particularly for automotive applications.